Undergraduate Program

Course Credit: 4

Introduction Statistics is about extracting meaning from data. This course will introduce techniques to collect, present, and summarize data to visualize the relationships in data and systematic techniques for understanding the relationships using mathematics. This course will also in- troduce the data science concept, the ability to extract information from data as a basis for evidence-based decision making is increasingly important in research and work-based situa- tions. Learning Outcomes During this course, students will develop a deeper understanding of the basic underlying features of modern methods of descriptive statistics and equip themselves with a data sum- marization tool kit which will enable them to apply their knowledge and skills to study other statistics courses and also to apply them in real-world tasks. This course contributes to the following program learning outcomes: (i) demonstrate a solid understanding of data science principles. (ii) demonstrate practical knowledge in data preparation, (iii) knowledge and tech- nical competence, (iv) the ability to use fundamental statistical knowledge, methodologies, and modern computational tools in a suitable and pertinent way, (v) problem-solving, (vi) comprehension of the balance between the di culty and correctness of the statistical measures used, and the equitability of the delivery of the solution (vii) an ability to amalgam knowledge and compliantly apply them to characterize, analyze and solve a wide range of problems, (viii) personal and professional awareness, (ix) the ability to contextualize outputs where data are drawn from diverse and evolving social, political, and cultural dimensions, (x) the ability to apply lifelong learning principles to new challenges.

Course Credit: 4

Introduction This is an introductory course on probability theory. This course attempts to provide basic concepts of set theory, experiment and sample space, and di erent approaches of de ning probability. It discusses useful laws of probability, conditional probability, Bayes rule, random variables and their distributions, and functions of random valuables. It also covers discussions on certain operators like mathematical expectation and generating function with properties and applications, and thorough discussions on commonly used probability distributions such as Binomial, Hyper geometric, Negative Binomial, Poisson, Normal, Exponential and Gamma distributions. Learning Outcomes Students who successfully complete this course should be able to: (i) understand the meaning of probability and probability experiment, (ii) use of Venn diagram to represent the result of set operations, (iii) understand the basic probability axioms and rules and the moments of discrete and continous random variables as well as be familiar with common named discrete and continous random variables, (iv) know how to derive the probability density function of transformations of random variables and use these techniques to generate data from vari- ous distributions, and (v) know how to calculate probabilities, and derive the marginal and conditional distributions of bivariate random variables.

Course Credit: 3

Introduction The Foundation of mathematics is a course that focuses on three key areas of mathematics: al- gebra, di erential equations, and discrete mathematics. These topics form the building blocks of many areas of mathematics and are essential for understanding advanced topics in statis- tics, mathematics, and computer science. The course will be taught through a combination of lectures, problem-solving sessions, and hands-on activities. Learning Outcomes After completion of the course, students are expected (i) to learn and understand the theory of numbers and di erential equations in detail, (ii) to understand and apply discrete math- ematical structures, such as sets, graphs, and combinatorics, to solve problems in computer science, cryptography, and other related elds iii) to develop problem-solving skills by ap- plying mathematical concepts to real-world problems, iv) to enhance analytical thinking and logical reasoning skills.

Course Credit: 3

Introduction The course aims to provide a rm foundation in the concepts and techniques of calculus, including basic functions and graphs and their properties, curve sketching, limits, continuity, di erentiation, relative extrema, and applications, Taylor Series. This course also introduces calculus from the two-dimensional world of single variable functions into the three-dimensional 17 world, and beyond, of multivariable functions. Students explore the following topics: vector geometry and the analytic geometry of lines, planes, and surfaces; calculus of curves in two or three dimensions, including arc length and curvature; calculus of scalar-valued functions of several variables, including the gradient, directional derivatives, and the Chain Rule; Lagrange multipliers and optimization problems. Learning Outcomes At the end of the course, the students will be able to (i) interpret a function from an alge- braic, numerical, graphical, and verbal perspective and extract information relevant to the phenomenon modeled by the function, (ii) understand the concept of limit and continuity of a function at a point graphically and algebraically using appropriate techniques, (iii) in- terpret the derivative of a function at a point as the instantaneous rate of change and as the slope of the tangent line, (iv) interpret the value of the rst and second derivatives as measures of increase and concavity of a function and understand the consequences of Rolle's theorem and the Mean Value Theorem for di erentiable functions, (v) perform operations with vectors in two and three-dimensional space and apply to analytic geometry, (vi) di er- entiate and integrate vector-valued functions and apply calculus to motion problems in two and three-dimensional space, (vii) determine the limits, derivatives, gradients, and integrals of multivariate functions.

Course Credit: 3

Introduction This course introduces the student to learn Integral Calculus, the techniques of integration, and to some of the applications of integration to physical problems and nding area, length of curve, volume, and surface area. Learning Outcomes Upon successful completion of this course, the students will be able to: i) describe the limit and continuity and derivative of a function of single and severable variables, ii) outline the local and absolute extrema of functions and their application, iii) use the concepts of de nite and inde nite integrals in a meaningful manner and use appropriate techniques for solving a variety of integration problems involving area, length, surface area, and volume, (iv) determine the area and volume by applying the techniques of double and triple integrals.

Course Credit: 3

Introduction Linear algebra can be used in pretty much any application that deals with more than ran- dom variable at a time than dealing with a random vector. Particularly, linear algebra will be heavily used in multivariate statistics course(s). Some matrix algebra will also be very convenient for potential studies in Markov chains and stochastic processes. Linear regression is a very common use of linear algebra as well. Learning Outcomes After successfully completion of this course the students will be able (i) to solve systems of linear equations using various methods including Gaussian and Gauss-Jordan elimination and inverse matrices, (ii) perform matrix algebra determinants and their properties, (iii) un- derstand real vector spaces and subspaces and apply their properties. Understand linear independence and dependence, (iv) nd basis and dimension of a vector space, (v) nd eigen- values and eigenvectors and use them in the applications, (vi) diagonalize, and orthogonally diagonalize symmetrix matrices, Evaluate the dot product, norm, angle between vectors, and orthogonally of two vectors, (vii) compute inner products on a real vector space and compute angle and orthogonality in inner product spaces, (viii) create orthogonal and orthonormal bases: Gram-Schmidt process and use bases and orthonormal bases to solve application prob- lems.

Course Credit: 4

Introduction This course provides an introduction to a broad range of economic concepts, description of theories and analytical techniques. It considers both microeconomics: the supply and demand based choices made by individual units (households and rms), and macroeconomics: the broader analysis of the economy. The use of supply and demand models will be the fundamental tools and the trade-o s and choices will be considered through comparison of costs and bene ts measures. Production and market structure will be analyzed at the rm level as well. Macroeconomic issues concerning the interaction of goods and services markets, labor supply and economic resources at an aggregate level (region or country level) will be discussed. Learning Outcomes After completion of the course, students are expected to (i) understand theories and principles in microeconomics including price theory, market structure, trade-o s, comparative advan- tage, factor markets, consumer theory, public goods, externalities and market failure, (ii) apply these principles to analyze economic issues, acquire quantitative skills used in economic analyses, (iii) understand scal policy and monetary policy of Bangladesh and address SDG goals.

Course Credit: 2

Introduction Programming with C/C++ course mainly focuses the programming language C. The course introduces machine level language, origin of C and applications of C programming in statistics. Learning Outcomes This course is expected to enable students to achieve understanding (i) how to connect C pro- gramming with statistics through owchart and programing code execution using compilation, link and run (ii) how to analyse descriptive statistics and data analysis using C programming.

Course Credit: 2

Introduction In today's data-driven world, business enterprises and industries can access more data than ever. However, they need skilled professionals who can manage and analyze data using various tools and techniques to make sense of all this data. Structured Query Language (SQL) is a widely used tool in data science for managing and querying data stored in relational databases. This course is designed to introduce students to SQL and its application in data science. Learning Outcomes Upon the completion of this course, students are expected to (i) understand the basic concepts of SQL, such as tables, queries, and basic SQL syntax; (ii) retrieve and manipulate data from databases using SQL queries, including ltering, sorting, and joining data from multiple tables; (iii) perform basic data analysis using SQL, including calculating summary statistics and aggregating data, (iv) Understand how SQL ts into the data science work ow.

Course Credit: 2

Course Credit: 3

Introduction This course introduces the theory of probability and sampling distribution for describing the behavior of random processes and further concentrates on using sampling distributions in statistical inference. Contents will involve deriving the theoretical properties of di erent distributions, examining the inter-relationship between them, and focusing on their applica- tions for exploratory analysis regarding selecting appropriate distributions for explaining the random behavior based on data and statistical inference. Learning Outcomes Upon completion of the course, students should (i) get acquainted with the main theoretical concepts probability and sampling distributions, (ii) learn about di erent generating function techniques and their role in mathematical derivation of distributions of functions of random variables, (iii) understand the role of the law of large numbers in approximating sampling distributions, (iv) learn about mathematical properties of common sampling distributions and inter-relationships and (v) understand the application of sampling distribution in statistical inference

Course Credit: 3

Introduction This course aims at providing an introduction to principles of actuarial science and mathemat- ics involved in it. Topics include role of insurance in economy, di erent instruments involved in actuarial computations including interest and discount rates, pricing and valuation of actu- arial products including annuities, amortization schedules, sinking funds, life assurances and premiums, among others. Learning Outcomes Upon completion of this course the students should be able to (i) understand the fundamental concepts of actuarial science and appreciate its role in economy, (ii) obtain concrete knowledge of underlying theories behind actuarial computations and (iii) understand how statistical and mathematical models are used in pricing and valuing actuarial products and their applications in various real-life actuarial problems.

Course Credit: 4

Introduction This course deals with fundamental concepts and techniques of statistical inference including estimation and tests of simple and composite hypotheses. A brief revision will also be given of some basic topics in probability theory as well as random variables.Learning Outcomes After completing the course, the students are expected to (i) know the most common para- metric and non-parametric distributions, (ii) be familiar with the concept of the likelihood principle, get used to the maximum likelihood, least square and the method of moments, (iii) be able to handle a parametric hypothesis testing problem, and (iv) to use the likelihood ratio method, have some knowledge of asymptotic statistics, that would help them to esti- mate and predict the SDG indicators upon the availability of baseline and midterm data with intervention.

Course Credit: 3

Introduction This course covers methodological and practical issues to design and analysis of experiments. Topics covered will include an introduction to design of experiments, completely randomised design, randomised complete block design, Latin square design and balanced incomplete block design. Learning Outcomes After completing the course, the students are expected to (i) identify the response variable, factor(s) of interest and nuisance factors, (ii) choose the most suitable experimental design for the given problem, (iii) set up the relevant null and alternative hypotheses. Obtain ANOVA tables, draw appropriate conclusions from them, (iv) construct appropriate (orthogonal) con- trasts, test their signi cance and make conclusions, i(v) carry out multiple comparisons among them and make recommendations, (vi) check model assumptions graphically and formally and apply a suitable transformation when needed, (vii) nd the appropriate sample size, and (viii) analyze data and state conclusions in the language of the problem posed.

Course Credit: 4

Introduction Demographic data contains valuable information about a country's socio-economic develop- ment, population growth and trend as well as the overall public health scenario. Thus De- mographic data comprises signi cant importance both in statistics and public health arena. This course focuses on de ning the demographic life events and explains the statistical analysis procedures. Learning Outcomes Upon successful completion of the course the students are expected to know (i) demographic data sources and collection procedure (ii) compute and interpret di erent types of fertility data (iii) measure di erent mortality statistics (iv) nuptiality measures (v) migration statistics and e ect on population growth

Course Credit: 3

Introduction This course introduces basic sampling techniques used in sample survey along with estima- tion procedures. This course covers simple random sampling, systematic sampling, strati ed random sampling, and cluster sampling. It also covers some special sampling designs. Learning Outcomes After completion of the course students are expected to (i) know about the necessity of sample survey, (ii) steps of conducting a survey, (iii) di ferent approaches to design a survey (iv) determine sample size, (v) and estimation techniques for the population parameters of interests.

Course Credit: 3

Introduction This course presents mathematical and elements of scienti c computing approach for solving mathematical problems that arises mainly in science and engineering applications. It deals with the theory and application of numerical approximation technique as well as their com- puter implementation. Mathematical methods, mainly Fourier analysis and Laplace trans- form, provide a foundation for further studies in disciplines in which Mathematics and Statis- tics may play important roles. Learning Outcomes Upon completion of this course the students should be able to (i) apply rigorous analytic, highly numerate approach and develop scienti c computer programs, (ii) develop the ability to apply Fourier series and Fourier Integrals to applied problems, and (iii) understand and apply in telecommunication that is closely related to Laplace transfrom.

Course Credit: 3

Introduction The study of mathematical analysis is indispensible for prospective students in di erent sectors of graduate level, specially, in science, engineering, economics and management sciences. This course contains basic properties of real numbers, sets, intervals; sequences and series of real numbers; di erent properties of limits, continuity, di erentiability and integration of a function; and basic concepts of measure theory and probability measure. Calculus is one of most fundamental courses in mathematical sciences and this is the prerequisite course for mathematical analysis. Learning Outcomes Upon completion of the course, students are expected to (i) develop the ability to analyze mathematical situations properly and precisely, (ii) learn to work comfortably with concepts that initially seemed so mysterious, (iii) know real numbers, sets, intervals; sequences and se- ries of real numbers and their convergence and divergence; Metric space and topological space and their properties; di erent properties of limits, continuity, di erentiability and Rieman integral of a function; and basic concepts of measure theory and probability measure.

Course Credit: 2

Introduction Any scienti c task without the knowledge of software is di cult to imagine and complete in the current scenario. R is a free software that is capable of handling mathematical and statistical manipulations. It has its own programming language as well as built-in functions to perform any specialized task. Students intend to learn the basics of R software in this course. Learning Outcomes After completion of the course students are expected to (i) be able to learn the basics of R programming language that includes understanding di erent types of R objects, (ii) writ- ing functions, reading/writing data of di erent formats, basic programming tools, and (iii) creating di erent types of plots. Students will also be introduced with Python.

Course Credit: 2

Introduction This course is designed to introduce computational methods and simulation techniques, which will be helpful for understanding theoretical concepts and practical applications of probability and sampling distributions, and statistical inference, including point and interval estimation and test of hypotheses. Learning Outcomes Upon completion of this course, the students should be able to (i) acquire detailed theoretical knowledge of common techniques of simulating data, (ii) write computers codes for simulat- ing data from di erent probability distributions and use these data to illustrate theoretical concepts related to sampling distributions and statistical inference and (iii) apply computa- tional methods required for tting probability distributions, conducting point and interval estimation, and performing test of hypothesis.

Course Credit: 2

Introduction Statistical computing is crucial in addressing real-life problems by utilizing statistical tech- niques and computational tools and providing a framework for understanding and making sense of the data. This computing course will discuss the applications of the statistical meth- ods discussed in the courses design of experiment and demography. Learning Outcomes By the end of the course, students will (i) have a solid understanding of the use of statistical software, e.g., R, and methods to analyze data in the area of experimental design and de- mography, (ii) learn how to apply the models related to di erent experimental designs (CRD, RBD, BIBD) to analyze the data in the eld of agriculture, industry, and medicine, (iii) learn to analyze and interpret demographic data, gaining insights into population trends, age-sex structures, fertility, mortality, migration, population projections and population growth rates, and (iv) be equipped with the skills to write programs to obtain statistical results from data in the relevant eld and draw meaningful conclusions from statistical analyses, which are essential for informed decision-making in various elds for public policy and planning.

Course Credit: 2

Course Credit: 4

Introduction This course deals with the concepts and techniques used in the factorial design. The course examines how to design factorial experiments, carry them out, and analyze the data. Exper- iments with random factors and nested and split designs are also discussed in this course. Learning Outcomes After completing the course, the students are expected to (i) design the experiments involving up to two and three factors with k levels, (ii) analyze the data from such experiments and interpret the results, (iii) understand the concept of blocking and confounding for the designs involving k factors with two levels, (iv) understand design and analysis the fractional factorial experiments for k factors each with two levels and the experiments involving random factors.

Course Credit: 3

Introduction This course introduces advanced sampling methods used in sample survey. It covers sampling of unequal clusters, two-stage sampling, multistage sampling, methods for estimating variance in complex surveys, and non-sampling errors.Learning Outcomes Upon completion of the course, students will able to (i) understand the necessity of multi-stage sampling (ii) formulate the mult-stage design (iii) know to methods for estimating variance in complex multi-stage design, and (iv) implement methods for designing a survey, particularly national level large scale survey.

Course Credit: 4

Introduction This introductory course gives an overview of regression types and details the application of multiple linear regression. This course covers the theory behind regression analysis, multiple linear regression, classical estimation and testing methods, and residual analysis. It also covers the formulation, interpretation and validation of linear regression models, and hands on use of a statistical package to see how the theory can be applied to answer a speci c research question. Learning Outcomes Upon completion of the course, students are expected to know (i) the concept of linear regres- sion and correlation, (ii) about model formulation and tting the model using least squares method, (iii) interpret the results, (iv) model diagnosis, (v) how to identify the best regression model, and vi) reqgression with qualitative variable in the model.

Course Credit: 3

Introduction This course covers the basic principles and methods of epidemiology, with an emphasis on critical thinking, analytic skills, and application to clinical practice and research. Topics include outcome measures, methods of adjustment, surveillance, quantitative study designs, and sources of data, analysis of exposure-disease relationship and causal inference. The course will provide tools for critically evaluating the literature and skills to practice evidence-based medicine. Learning Outcomes Upon completion of the course, students should (i) familiar with epidemiologic terminology, outcome measures, and study designs, (ii) familiar with the methods for estimating asosciation measures such as incidence and prevalence rate, risk ratio, odds ratio etc., (iii) appreciate application of epidemiologic methods in the studies of both infectious and chronic diseases and current public health issues.

Course Credit: 3

Introduction This course deals with the analysis of survival or failure time data, which are commonly encountered in scienti c investigations. It is being extensively used in medicine, clinical trials, biological and epidemiological studies, engineering, economics, and social sciences. This course provides an opportunity for students to learn lifetime probability distributions that are useful for modeling tine-to-event data. Topics include lifetime distributions and non-parametric and parametric approaches for analyzing time-to-event data.Learning Outcomes After successful completion of the course, students are expected to (i) understand the basic features of censored lifetime data, survival function, and hazard functions, (ii) distributions of lifetime data, and (iii) both parametric and non-parametric estimation of survival and hazard functions.

Course Credit: 3

Introduction A branch of statistics has been developed to draw conclusion in a short time and cost-e ective way regarding the population of interest which is ubiquitously known as statistical inference. It facilitates both parametric and nonparametric approaches under the umbrella of classical and Bayesian paradigms. Learning Outcomes After completion of this course, students (i) will be able to draw statistical inference both in classical and Bayesian framework, (ii) will have ample skills in handling data to meet the inferential needs in diverse areas of applications.

Course Credit: 3

Introduction The objective of the course is to introduce several useful multivariate techniques, and making strong use of illustrative examples. Learning Outcomes On a general level the students should be able to understand the concept of analysing multi- variate data. They should be familiar with a basic minimum level of matrix competency and with general aspects of handling multivariate data. On successful completion of the course the student will (i) appreciate the range of multivariate techniques available, (ii) be able to summarize and interpret multivariate data, (iii) have an understanding of the link between multivariate techniques and corresponding univariate techniques, (iv) be able to use multivari- ate techniques appropriately, undertake multivariate hypothesis tests, and draw appropriate conclusions.

Course Credit: 3

Introduction The research methodology course is fundamental to teaching students the theoretical and practical skills required to conduct research in various elds. The course introduces students to the fundamental principles of research design, data collection, data analysis, and report writing. It will cover a range of research approaches, including quantitative, qualitative, and mixed methods. Learning Outcomes By the end of the course, students will learn how to formulate research questions, develop hypotheses, choose appropriate research methods, and collect and analyze data. Students will also learn about the various data collection techniques, such as surveys, interviews, observa- tions, and statistical techniques for analyzing data. In addition to the theoretical components of the course, students will apply their knowledge through practical exercises and projects. Students should become competent in planning, conducting, evaluating, and presenting a research project.

Course Credit: 2

Introduction This is an introductory course of the three popular statistical packages. Students will learn to implement di erent statistical techniques using these packages. Learning Outcomes A student is expected to (i) achieve basic skills on Stata/SPSS codes, (ii) learn how to analyse data coming from a survey/experimental study, (iii) be able to manage primary/secondary data to answer a speci c query, (iv) learn implementation of a wide range of statistical tech- niques using SPSS and Stata.

Course Credit: 2

Introduction Python has become one of the most popular programming languages for data science due to its simplicity, versatility, and robust data analysis and machine learning capabilities. As organizations continue to digitize and generate increasingly large amounts of data, open- source tools like Python can enable data manipulation and exploration, machine learning, and statistical analysis in a scalable manner. This course provides a gentle introduction to programming in Python and its applications in data science and analytics. Learning Outcomes At the end of this course, students are expected to (i) understand the fundamentals of Python programming, (ii) produce insights from data through exploratory data analysis, and (iii) create e ective data visualizations using Python.

Course Credit: 2

Introduction This statistical computing course provides a comprehensive understanding of applications of the methods covered in the courses design and analysis of experiments, epidemiology, and lifetime data analysis to real-life problems, equipping students with the tools and knowledge to address real-life problems across various elds. By achieving these skills, students are expected to make data-driven decisions and contribute to solving the complex challenges faced by society today. Learning Outcomes Upon completion of the course, students should (i) understand how to use R or Stata to t linear models used in factorial experiments, (ii) able to perform bivariate analysis of epidemi- ological studies, and (iii) able to obtain non-parametric and parametric estimates of survival and cumulative hazards functions; (iv) able to interpret the statistical results for decision- making processes related to healthcare, engineering, nance, and more.

Course Credit: 2

Introduction This computing course is based on applications of regression models, multivariate methods, and statistical inference to real-life problems. By mastering these techniques for solving real- life problems, students will be equipped to analyze complex datasets, uncover meaningful insights, and contribute to data-driven decision-making processes in academia, industry, and research. Learning Outcomes On successful completion of this course, the students are expected to: (i) understand the applications of linear regression models, (ii) able to make inference about mean vector and apply multivariate linear regression models to real-life problems, and (iii) perform parametric and nonparametric hypothesis tests and corresponding con dence intervals, (iv) e ectively utilize statistical computing tools and software packages such as R, Stata, or Python to implement regression models, multivariate methods, and statistical inference techniques, (v) to manipulate and analyze large datasets, visualize results, and present their ndings clearly and concisely.

Course Credit: 2

Course Credit: 3

Introduction This module covers two important parts, one is advanced probability and another is stochastic processes. The module begins with a brief overview of modern probability and convergence of random variable and then moves on to the stochastic process to describe Markov chains, Poisson process, continuous time Markov chain and renewal process. Learning Outcomes Upon completion of the course the students are expected to (i) know how to apply advanced probability in real life problems and how to give prediction using transition probability and gamblers ruin theory, (ii) formulate simple stochastic process models in the time domain and provide qualitative and quantitative analyses of such models, (iii) understand the applications of stochastic processes in real life situations.

Course Credit: 3

Introduction The course provides a broad but thorough introduction to the methods and practice of basic statistical machine learning and its core methods, models, and algorithms. Learning Outcomes After completing the course, students will have the knowledge and skills to (i) the concept of Arti cial Intelligence, Big Data, and their areas. (ii) describe a number of models for su- pervised and unsupervised machine learning and regularized modeling techniques (iii) assess the strength and weaknesses of each of these models, (iv) know the underlying mathematical relationships within and across statistical learning algorithms, (v) develop and implement op- timization methods for training of statistical models, (vi) design decision and optimal control problems to improve performance of statistical learning algorithms, (vii) design and imple- ment various statistical machine learning algorithms in real-world applications, (viii) evaluate the performance of various statistical machine learning algorithms, (ix) demonstrate a working knowledge of dimension reduction techniques. (x) identify and implement advanced compu- tational methods in machine learning.

Course Credit: 3

Introduction Multivariate analysis skills have been recognized as part of the key requisites for statistical analysts. The complexity of most phenomena in the real world requires an investigator to collect and analyze observations on many di erent variables instead of a single variable. The desire for statistical techniques to elicit information from multivariate dimensional data thus becomes essential and crucial for data analysts. This course focuses on multivariate methods based on normal theory. It gives students working knowledge on how to analyze data and solve problems involving measurements of p variables on each of n subjects. Learning Outcomes Having successfully completed this course, students should be able to (i) know the theoretical concept of advanced multivariate methods, (ii) know about data-dimension concept, necessity and techniques (iii) apply the methods for analysing real life problem and interpret the results.

Course Credit: 4

Introduction This course covers a range of econometric methods required to conduct empirical economic research and understand applied econometric results. Topics include models for panel data, simultaneous equations models, models with lagged variables, and limited dependent variables. Learning Outcomes On successful completion of this course the students should be able to (i) understand the basic principles of econometric analysis and econometric model building, (ii) gain theoretical understanding of the methods/models needed for econometric research including their un- derlying assumptions, advantages and limitations and (iii) understand how to use di erent econometric tools in real-world economic problems and interpret ndings.

Course Credit: 3

Introduction This course deals with modeling survival or failure time data. Both parametric and semi- parametric approaches are discussed in the course. A brief introduction to competing risks and frailty models is also provided. Learning Outcomes After successful completion of the course, students are expected to (i) formulate a regression model for time-to-event data and interpret the model ts, (ii) be able to analyze competing risks and frailty model.

Course Credit: 4

Introduction This course deals with the concepts and techniques used in the industry to maintain the quality of the process. Concepts of di erent types of control charts and their application in industry are discussed in this course. Basic concepts of acceptance sampling and how to design an acceptance sampling are also discussed. The concept and solution procedure of linear programming problem, duality and sensitivity analysis are discussed. Introduction of game theory, in particular the two person zero sum game are discussed. Learning Outcomes On successful completion of this course, the student is expected to: (i) understand the basic ideas of control chart techniques; (ii) apply and interpret control charts for variables and attributes; (iii) design acceptance sampling plan; and (iv) nd optimal solution using linear programming problem.

Course Credit: 3

Introduction This is an introductory course of time series theory. The objective of this course is to equip students with various classical time series models, deriving their properties, inference methods and forecasting techniques for analyzing time series data. From computational point of view, it aims to demonstrate the theory with real datasets. Conclusions and proofs are given for some basic formulas and models; these enable the students to understand the principles of time series theory. Learning Outcomes On successful completion of this course, the students are expected to (i) identify the main components of the time series and apply a suitable exponential smoothing technique to forecast with a variety of time series models such as additive or multiplicative seasonal models by updating the components, (ii) formulate time series regression models for the series with trend and seasonal components and make forecasts from these models, (iii) Identify ARIMA models tentatively from ACF and PACF, (iv) estimate parameters of the ARIMA models by the method of moments, least squares method and maximum likelihood method, and (v) check the adequacy of the model and make forecasts.

Course Credit: 3

Introduction This course deals with di erent statistical models for the analysis of quantitative and quali- tative data, of the types usually encountered in research. Learning Outcomes After completing the course students will be familiar with (i) the exponential family of distri- butions, (ii) the class of generalized linear models (GLM) as regression models with responses from the exponential family of distributions, (iii) the concepts of link functions for modeling the correspondence between the expected value of the responses and covariates and of vari- ance functions for specifying the correspondence between the expected values and variances of the responses, (iv) analyzing data from important special cases of GLMs, in particular logistic regression and Poisson regression, and (v) extensions of the GLM framework using quasi likelihood based on specied link and variance functions.

Course Credit: 3

Introduction O cial statistics is a branch of statistics that deals with the collection, analysis, interpretation, and dissemination of data produced by government agencies and other o cial bodies. Its purpose is to provide accurate and reliable information on various aspects of society, such as the economy, population, health, education, and crime. O cial statistics are used for various purposes, including policy-making, research, business-decision making etc. Learning Outcomes After completing this course, students are expected to (i) use statistical software (R, Python) to apply several multivariate and machine learning techniques to analyze real-life data, (ii) understand how to evaluate models generated from data, (iii) apply the algorithms to a real problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models. (iv) interpret the results and write for scienti c publication.

Course Credit: 2

Introduction This computing course is designed to teach students the applications of di erent multivari- ate techniques, methods in quality control and operations research, and machine learning techniques to solve real-life problems. By mastering these techniques, students will be able to extract meaningful insights and make data-driven decisions in various elds of research, policy-making, and planning. Learning Outcomes After completing this course, students are expected to (i) develop a strong foundation in multivariate techniques (PCA, factor analysis, discrimination analysis) and their applications, enabling them to analyze complex datasets, identify patterns, and extract meaningful insights for decision-making, (ii) gain the skills necessary to implement statistical process control techniques, optimize quality control processes, and ensure adherence to prede ned quality standards, (iii) learn how to formulate and solve mathematical models, optimize processes, and make data-driven decisions to improve operational e ciency and resource allocation, (iv) gain an understanding of key machine learning algorithms, including regression, classi cation, and clustering, and (v) learn how to train and evaluate models, perform feature selection, and apply machine learning techniques to real-life problems, ranging from image recognition to predictive analytics.

Course Credit: 2

Introduction This statistical computing course focuses on real-life applications of the topics covered in the courses: time series analysis and econometrics methods. The course will equip students with the tools and techniques to analyze and interpret time series, and economic and nancial data, make forecasts, estimate economic relationships, and contribute to economic research and policy analysis by integrating statistical software R, Stata, or Python. Learning Outcomes After completing this course, students are expected to (i) acquire pro ciency in using statisti- cal computing tools and software packages to implement time series analysis and econometric methods, (iii) simulate time series objects and explore its various features, (ii) t di erent time series models to real-life data and identify the best models for forecasting by comparing di erent models based on AIC, BIC, and forecasting error, (iv) t regression model for econo- metric data, and perform tests for multicollinearity, heteroscedasticity, autocorrelation, and also compare between compatible models, (v) t dynamic regression models and simultaneous equation models to various real-life econometric data, (vi) gain a comprehensive understand- ing of time series and econometric models, and the skill to interpret and evaluate results and make informed decisions based on their analyses.

Course Credit: 2

Introduction This course deals with applying methods related to generalized linear models and lifetime data analysis to real-life data from diverse areas such as medicine, biological science, engineering, and social science. In addition, this course covers the use of statistical software and packages (e.g., R or Stata) of each model for computing results from real-life data. By combining com- putational tools with advanced statistical models, students will develop the skills to analyze complex data, model relationships, and draw meaningful conclusions in various domains. Learning Outcomes Upon completion of the course, students will (i) develop a solid understanding of di erent kinds of models under GLMs, (ii) able to identify appropriate GLM formulations for di erent response variables and t them using computational tools, and interpret the results in the context of the problem at hand, (iii) gain expertise in survival analysis techniques and models (both parametric and semiparametric models), applying them to analyze and interpret time- to-event data e ectively, and (iv) achieve skills and knowledge necessary to contribute to advancements in healthcare, engineering, nance, and various other domains where statistical modeling and data analysis play a vital role.

Course Credit: 2

Course Credit: 3